This paper lays the grounds for a new methodology for detecting thermal discomfort, which can potentially reduce the building energy usage while improving the comfort of its inhabitants. The paper describes our explorations in automatic human discomfort prediction using physiological signals directly collected from a buildings inhabitants. Using infrared thermography, as well as several other bio-sensors (galvanic skin response, heart rate tracker, respiration rate tracker), we record a building’s inhabitants under various thermal conditions (hot, cold, neutral), and consequently build a multimodal model that can automatically detect thermal discomfort.

The paper makes two important contributions. First, we introduce a novel dataset, consisting of sensorial measurements of human behavior under varied comfort/discomfort conditions. The change in physiological signals of the human body are monitored for several subjects, for different comfort levels in an indoor environment. Second, using the dataset obtained in the first step, we build a model that identifies the relationship between human factors, as tracked through infrared thermography and other bio-sensors, and environmental conditions related to discomfort. Third, we measure the correlation between sensorial measurements collected from the user and self-reported levels of discomfort, and hence identify the sensorial measurements that are predictive of human discomfort. The final goal is to automatically predict the level of discomfort of a building inhabitant without any explicit input from the user.

This human-centered discomfort prediction model is expected to enable innovative adaptive control scenarios for a built environment conditions in real time, as well as a significant reduction in building energy usage directly related to human occupancy and their desired comfort levels.

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